The Impact of Racial Bias in Patient Care and Medical Education: Let's Focus on the Educator.

Introduction: Racial bias in health care is well documented. Research shows the presence of racial bias among health care providers. There is a paucity of workshops focused on racial bias effects in health professions educators. Method: Two to three workshops were delivered to a diverse group of clinical educators from three programs at a major academic institution. Each workshop included a brief multimedia presentation followed by a facilitated group discussion. Participants completed the online Implicit Association Test (IAT), a baseline demographic questionnaire, and a brief post-then-pre questionnaire. Results: Twenty-four faculty participated in the study (six physicians, eight nurse practitioners, 10 physician assistants). Nineteen (90%) were women, 18 (86%) were White, nine (43%) had more than 10 years of experience as educators, and seven (35%) had previously participated in a biases program. Seventeen completed the IAT. Sixteen educators agreed or strongly agreed that bias has a significant impact on patients' outcomes at the end of the workshop compared to 17 before the workshop. Seventeen educators agreed or strongly agreed that recognizing their own racial bias would positively alter their teaching practice after the workshop compared to 15 before the workshop. Discussion: This series of workshops was created to fill a gap regarding the impact of racial bias on patient outcomes, health disparities, and health professions education. The impact of racial bias in health professions education and the long-term impact of awareness and knowledge of racial bias in education are areas needing further evaluation.

Predicting Self-Rated Health Across the Life Course: Health Equity Insights from Machine Learning Models.

BACKGROUND: Self-rated health is a strong predictor of mortality and morbidity. Machine learning techniques may provide insights into which of the multifaceted contributors to self-rated health are key drivers in diverse groups. OBJECTIVE: We used machine learning algorithms to predict self-rated health in diverse groups in the Behavioral Risk Factor Surveillance System (BRFSS), to understand how machine learning algorithms might be used explicitly to examine drivers of self-rated health in diverse populations. DESIGN: We applied three common machine learning algorithms to predict self-rated health in the 2017 BRFSS survey, stratified by age, race/ethnicity, and sex. We replicated our process in the 2016 BRFSS survey. PARTICIPANTS: We analyzed data from 449,492 adult participants of the 2017 BRFSS survey. MAIN MEASURES: We examined area under the curve (AUC) statistics to examine model fit within each group. We used traditional logistic regression to predict self-rated health associated with features identified by machine learning models. KEY RESULTS: Each algorithm, regularized logistic regression (AUC: 0.81), random forest (AUC: 0.80), and support vector machine (AUC: 0.81), provided good model fit in the BRFSS. Predictors of self-rated health were similar by sex and race/ethnicity but differed by age. Socioeconomic features were prominent predictors of self-rated health in mid-life age groups. Income [OR: 1.70 (95% CI: 1.62-1.80)], education [OR: 2.02 (95% CI: 1.89, 2.16)], physical activity [OR: 1.52 (95% CI: 1.46-1.58)], depression [OR: 0.66 (95% CI: 0.63-0.68)], difficulty concentrating [OR: 0.62 (95% CI: 0.58-0.66)], and hypertension [OR: 0.59 (95% CI: 0.57-0.61)] all predicted the odds of excellent or very good self-rated health. CONCLUSIONS: Our analysis of BRFSS data show social determinants of health are prominent predictors of self-rated health in mid-life. Our work may demonstrate promising practices for using machine learning to advance health equity.

Identification of Racial Inequities in Access to Specialized Inpatient Heart Failure Care at an Academic Medical Center.

BACKGROUND: Racial inequities for patients with heart failure (HF) have been widely documented. HF patients who receive cardiology care during a hospital admission have better outcomes. It is unknown whether there are differences in admission to a cardiology or general medicine service by race. This study examined the relationship between race and admission service, and its effect on 30-day readmission and mortality Methods: We performed a retrospective cohort study from September 2008 to November 2017 at a single large urban academic referral center of all patients self-referred to the emergency department and admitted to either the cardiology or general medicine service with a principal diagnosis of HF, who self-identified as white, black, or Latinx. We used multivariable generalized estimating equation models to assess the relationship between race and admission to the cardiology service. We used Cox regression to assess the association between race, admission service, and 30-day readmission and mortality. RESULTS: Among 1967 unique patients (66.7% white, 23.6% black, and 9.7% Latinx), black and Latinx patients had lower rates of admission to the cardiology service than white patients (adjusted rate ratio, 0.91; 95% CI, 0.84-0.98, for black; adjusted rate ratio, 0.83; 95% CI, 0.72-0.97 for Latinx). Female sex and age >75 years were also independently associated with lower rates of admission to the cardiology service. Admission to the cardiology service was independently associated with decreased readmission within 30 days, independent of race. CONCLUSIONS: Black and Latinx patients were less likely to be admitted to cardiology for HF care. This inequity may, in part, drive racial inequities in HF outcomes.

Addressing racial bias in wards.

Health disparities fall along racial lines, in part, due to structural inequalities limiting health care access. The concept of race is often taught in health professions education with a clear biologic underpinning despite the significant debate in the literature as to whether race is a social or biologic construct. The teaching of race as a biologic construct, however, allows for the simplification of race as a risk factor for disease. As health care providers, it is part of our professional responsibility and duty to patients to think and talk about race in a way that is cognizant of broader historical, political, and cultural literature and context. Openly discussing the topic of race in medicine is not only uncomfortable but also difficult given its controversies and complicated context. In response, we provide several evidence-based steps to guide discussions around race in clinical settings, while also hopefully limiting the use of bias and racism in the practice of medicine.

When Race Matters on the Wards: Talking About Racial Health Disparities and Racism in the Clinical Setting.

INTRODUCTION: There is a growing body of literature illustrating the negative impact of racial bias on clinical care. Despite the growing evidence, medical schools have been slow to make necessary curricular changes. Most attempts to educate on racial health disparities focus on transferring knowledge and do not foster the development of skills to understand one's own bias or address bias and racism in the clinical setting. To address this, we developed a small-group, case-based curriculum for rising third-year medical students. METHODS: This session was designed to be delivered in concurrently run, 1-hour small-group sessions, with each small group ideally comprising no more than 10 students and one facilitator. The curriculum was integrated into an existing 3-week clerkship preparation course for 122 students during the 2015-2016 academic year. The session materials include a facilitator's guide and three cases for discussion. RESULTS: The session was evaluated using a 6-point Likert scale (1 = poor, 6 = exceptional). Students rated this session overall a 4.28 out of 6 (N = 79). Qualitative feedback varied, with the most common theme focusing on the need for more time to discuss this topic. DISCUSSION: Though one session before starting clinical clerkships is not enough to maintain the practice of sustained critical thinking regarding bias and racism in clinical medicine, this session is a starting point for curriculum developers looking to use an evidence-based approach to racial bias in clinical care.